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Abstract Microbial genomes encode numerous biosynthetic gene clusters, yet most remain experimentally uncharacterized, particularly large non-ribosomal peptide synthetase (NRPS) clusters that resist conventional cloning. Existing refactoring approaches are labor-intensive, depend on de novo DNA synthesis and are difficult to scale.
Abstract Generative pre-trained transformers (GPT) models provide powerful platforms for pioneering de novo protein design (DNPD). GPT-based DNPD involves three procedures: a) finetuning the model with proteins of interest; b) generating sequence candidates with the finetuned model; and c) prioritizing the sequence candidates. Existing prioritization strategies heavily rely on sequence identity, undermining the diversity.
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